Detection of Arc in the Transmission Power Lines

One- and three-phase automatic reclosing of power lines, after short circuit shutdown, is a very effective way to improve the reliability of power delivery. Re-closing while the fault is not cleared can be dangerous for some electrical appliances. This paper provides a description of two optimization methods concerning the detection of arc in the transmission power lines. The first method is based on least squares approach (LS) and second on total least squares approach (TLS). In order to identify a short-circuit arc, it is proposed to use the degree of distortion of the voltage curve at the beginning of the line, therefore the both methods are based on the estimation of parameters in real-time voltage signals and an analysis of error estimation. An artificial neural network has been proposed to solve the problem in real time. The problems are formulated as optimization tasks and solved using the steepest descent continuous-time optimization algorithm. The network based on the TLS criterion realizes the optimization process under the assumption that the signal model can also be deteriorated (frequency or sampling interval fluctuation and so forth). In comparison to LS estimation, the TLS estimation effect is more reliable when higher sampling frequency and a wider sampling window is applied. The benefit of this research is the innovative possibility of fast detection of arcing faults in real time.